With the popularity of ultra-high resolution video images in the market, video and image codecs causes an increasing bandwidth burden to an external memory, and a large amount of data exchanges with the external memory also increases chip power consumption. Video data compression is a way to solve the problem of bandwidth burden. Frame compression methods generally include three types: lossy compression, lossless compression and hybrid compression consisting of lossy and lossless compression. Lossy compression generally adopts a method of quantizing compressed data. Lossless frame compression is usually composed of two stages: prediction and entropy coding. The processing method of the first stage generally includes two types, one is prediction processing, e.g. spatial prediction, which is utilized to get a difference between data to be compressed and a predicted value, and then the second stage of processing will be performed; and the other is transform processing, which is utilized to transform the data to the frequency domain, and then the second stage of processing will be performed. The processing method of the second stage is usually entropy coding processing, in which variable length codes are typically used to encode, so as to achieve data compression.
Lossy compression typically achieves an increase in compression ratio at the expense of loss of video quality, which degrades the quality of a current video frame. If the current video frame is used as a reference video frame for subsequent video frames in a video codec, a quality loss of the current video frame will be propagated in the subsequent video frames, thereby decreasing the quality of the video sequence. Currently, the conventional lossless compression methods either have a low overall compression ratio, or have a high computational complexity.
Therefore, it is desirable to improve the existing methods of video data compression.